gStream: Graph-Based Sequential Change-Point Detection for Streaming Data
Uses an approach based on k-nearest neighbor information to sequentially detect change-points. Offers analytic approximations for false discovery control given user-specified average run length. Can be applied to any type of data (high-dimensional, non-Euclidean, etc.) as long as a reasonable similarity measure is available. See references (1) Chen, H. (2019) Sequential change-point detection based on nearest neighbors. The Annals of Statistics, 47(3):1381-1407. (2) Chu, L. and Chen, H. (2018) Sequential change-point detection for high-dimensional and non-Euclidean data <arXiv:1810.05973>.
||R (≥ 3.0.1)
||Hao Chen and Lynna Chu
||Hao Chen <hxchen at ucdavis.edu>
||GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Please use the canonical form
to link to this page.